Toward the 4th Agenda 2030 Goal: AI Support to Executive Functions for Inclusions

Toward the 4th Agenda 2030 Goal: AI Support to Executive Functions for Inclusions

Rita Tegon (Liceo Classico “A. Canova”, Treviso, Italy)
Copyright: © 2021 |Pages: 25
DOI: 10.4018/978-1-7998-7638-0.ch025
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The 2030 Agenda settles inclusion as a crucial goal. The index for inclusion underlines a set of resources to guide educational agencies through a process of inclusive development. One interesting model to achieve it is the Universal Design of Learning (UDL) framework, whose roots lie in the field of architecture and cognitive neuroscience. It provides options to enhance the executive functions also with the support of assistive technologies: studies have recently contributed to investigate how AI-innovated Educational Management Information Systems (EMIS), apps, and learning assessments can offer to the teachers the opportunities to differentiate and individualize learning, to diagnose factors of exclusion in education, and predict dropout, dyslexia, or autism disorder. After a discussion on the state of research and on the preparatory concepts, the chapter presents examples of AI-supported tools, and how they can scaffold executive functions; it wants also to urge a future-oriented vision regarding AI and to re-think the role of education in society.
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Models For Inclusion In Education: The Universal Design For Learning908

In the last decades, there has been a growing effort to implement inclusive education around the globe.

Key Terms in this Chapter

UDL: Universal Design for Learning is a way of thinking about teaching and learning that helps give all students an equal opportunity to succeed.

Neural Network: Also called a neural net, this is a computer system designed to function like the human brain. Although researchers are still working on creating a machine model of the human brain, existing neural networks can perform many tasks involving speech, vision, and board game strategy.

Cognitive Computing: A computerized model that mimics the way the human brain thinks. It involves self-learning through the use of data mining, natural language processing, and pattern recognition.

Data Science: Drawing from statistics, computer science and information science, this interdisciplinary field aims to use a variety of scientific methods, processes, and systems to solve problems involving data.

Bias: Machine learning bias, also sometimes called algorithm bias or AI bias, is a phenomenon that occurs when an algorithm produces results that are systemically prejudiced due to erroneous assumptions in the machine learning process.

Educational Data Mining: Is concerned with developing, researching, and applying computerized methods to detect patterns in large collections of educational data.

General AI: An AI that could successfully do any intellectual task that any given human being currently can. This is sometimes referred to a strong AI, although they aren’t entirely equivalent terms.

Weak AI: Also called narrow AI, this is a model that has a set range of skills and focuses on one particular set of tasks. Most AI currently in use is weak AI, unable to learn or perform tasks outside of its specialist skill set.

Executive Functions: The executive functions are a set of processes that all have to do with managing oneself and one's resources in order to achieve a goal. It is an umbrella term for the neurologically-based skills involving flexibility, memory and self-regulation.

Deep Learning: The ability for machines to autonomously mimic human thought patterns through artificial neural networks composed of cascading layers of information.

EMIS: An EMIS can be defined as a system for the collection, integration, processing, maintenance and dissemination of data and information to support decision-making, policy-analysis and formulation, planning, monitoring and management at all levels of an education system.

Data Mining: The process of analyzing datasets in order to discover new patterns that might improve the model.

Algorithms: A set of rules or instructions given to an AI, neural network, or other machines to help them learn on its own; classification, clustering, recommendation, and regression are four of the most popular types.

Machine Learning: A facet of AI that focuses on algorithms, allowing machines to learn without being programmed and change when exposed to new data.

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